1. Raster Analysis: Intro
Basics
Applying Boolean logic in raster
Comparable vector operations
Raster modeling and statistics
Where raster distinguishes itself

2. Raster vs. Vector https://experimentalcraft.wordpress.com/tag/gis/
Source
Source

3. Raster always has dimensions—the pixel size

4. Converting points to raster
Rasterised point layers are not ‘compact’ – one value per pixel
Point has area equal to cell size

5. Lines to raster
Lines are seen as contiguous pixels

6. Question: how would you calculate the area of forestland in the raster
Vector has ownership data stored as an attribute

7. Vector to raster conversion
Similar to scanning – specify cell size (pixels) and the attribute used
- Vector GIS handles attributes more effectively

8. Polygons to raster areas have similar adjacent pixels
Attribute table shows the number of pixels in each value, these are graphed in a histogram

9. How to Really Mess up with GIS
Regular grid, 16 per -> irregular shapes/sizes
Numbers in vector are total count for area, not density, leading to serious visual misrepresentation

10. Neighborhood Operations
Operation: Summation (including value of focal cell)
Neighborhood size: 3 x 3 rectangle
Because an animal won’t use just one cell…
e.g. to establish available food supply for wildlife

11. Neighborhood Operations
Summation (including value of focal cell)
Each cell’s neighborhood calculated in turn.
Redundancy in system: you might have screwed one cell up, but it won’t matter as much
Other common applications:
Data simplification (smoothing)
Terrain analysis (local relief / roughness)
Site selection

12. Raster Pixel Depth Capacity for precision Size Camera runs on 8-bit

13. Boolean Logic in Raster Analysis
“Create an expression reducible to true/false”
Binary examples
Landscape examples

14. Raster Math
2 + 2 = 4
2 + 3 = 5
2 + 4 = 6 2 3 4 5 2 3 4 4 5 6 7 8 9
Raster math is normal math

15. Boolean Logic: AND 0 * 1 = 0 (false) 1 * 0 = 0 (false)
1 * 1 = 1 (true)
Looking for a “1”
Input 1:
Input 2:
Output: 1 1 1
Objective: Quality Dining
Good Reviews
Open Tables
Potential Dining

16. Boolean Logic: OR 0 + 0 = 0 (false) 0 + 1 = 1 (true) 1 + 0 = 1 (true)
Looking for => “1”
Input 1:
Input 2:
Output: 1 1 1 2
Objective: Food in stomach (but willing to wait if it’s good)
Good Reviews
Open Tables
Potential Dining

17. Boolean Logic: XOR 0 + 0 = 0 (false) 0 + 1 = 1 (true) 1 + 0 = 1 (true)
Looking for = “1”
Input 1:
Input 2:
Output: 1 1 1 2
Objective: No second date
Good Reviews
Open Tables
Potential Dining

18. Boolean Logic: NOT 0 - 1 = -1 (false) 1 - 0 = 1 (true)
Looking for = “1”
Input 1:
Input 2:
Output: 1 1 -1 1
Objective: questionable
Good Reviews
Open Tables
Potential Dining

19. Raster Model: Roads vs. Cover
Roads bad, conifer cover good
Input 1:
Input 2:
Output: 50
100 75
120
150 80 50
100 20 75 10 40 60 80
100
200 70
150
130
140
180
170
Objective: questionable
Road Distance
Conifer Cover
Habitat Quality

20. Now pick it apart, what’s the problem?
What is a Habitat Model?
Basic needs: food, water, shelter
Each variable ranked 0-100
Sum of the inputs = overall quality
Now pick it apart, what’s the problem?
100 50 75 40
100 50 75 50 75
100 40
200
150
225
300
180
Habitat Model
Food
Water
Shelter

21. One Step Up: >0 Requirement
Basic needs: food, water, shelter
If any of the inputs are zero, output = 0
(as no shelter = dead)
100 50 75 40
100 50 75 50 75
100 40
150
225
300
180
Habitat Model
Food
Water
Shelter

22. Euclidean Distance Straight-line distance from point, line, or poly
Point in the center of the 0
1m raster grid
Symbology broken into 1m classes
1.4
2.8
4.2
5.6

23. Using Euclidean Distance
Distance from roads vs. Distance to Rivers
Input 1:
Input 2:
Output: 50
100 75
120 45 80 50
100 20 75 10 40 60
River Distance
(under 80 good)
Road Distance
(under 80 bad)
Acceptable Habitat

24. Clip vs Euclidian Distance
Clip: Is it, or is it not, within distance X
Yes or no
Will chop a feature in half
Raster: Average distance from pixel to X
Distance at whatever precision specified
But never absolutely precise (vs. point)

25. Pesky Parameter Problems
The sly and elusive lynx avoids roads….

26. Expert-Based Habitat Modeling
Ask a trapper where (s)he sees bears in spring
Identify key characteristics
Distance to roads
Distance to eskers
Distance to old forest
Distance to swamp
Extrapolate to the landscape
Ex. the wolves used to be afraid of the roads, now they’ve gotten used to them (made up)

27. Work-Through Lacking experts, any parameters will do
‘Twas brillig, and the slithy toves    
Did gyre and gimble in the wabe;
All mimsy were the borogoves,    
And the mome raths outgrabe.
“Beware the Jabberwock, my son    
The jaws that bite, the claws that catch!
Beware the Jubjub bird, and shun    
The frumious Bandersnatch!”
He took his vorpal sword in hand;    
Long time the manxome foe he sought—
So rested he by the Tumtum tree,    
And stood awhile in thought.
And, as in uffish thought he stood,    
The Jabberwock, with eyes of flame,
Came whiffling through the tulgey wood,    
And burbled as it came!
One, two! One, two! And through and through    
The vorpal blade went snicker-snack!
He left it dead, and with its head    
He went galumphing back.
“And hast thou slain the Jabberwock?    
Come to my arms, my beamish boy!
O frabjous day! Callooh! Callay!”    
He chortled in his joy.
The purpose of this is to show something that raster can do that vector can’t

28. Objective
Identify routes that the Jabberwocky is likely to use as it travels from Purden Provincial Park to Aleza Lake Ecological Reserve and prioritize them according to the % of the landscape that may be devoted to Jabberwocky conservation

29. Toolset: Corridor Design
Assumption: animal movement follows path of least risk (food, water, cover)
Food, water, cover differ by species
By finding routes that provide food, water, cover, we can maintain a travel corridor between patches

30. Parameters Jabberwocky will prefer to be
During the summertime, when adventure-seeking knights (and graduate students) roam the countryside, the Jabberwocky tends to avoid travelled roads. Rivers and swamps are its preferred haunts, where Bandersnatches and Jubjub birds are present to keep watch for would-be heroes. Finally, the creature is easily scared off by its arch nemesis the feller-buncher, and does not return to a stand until the area has been successfully regenerated.
Jabberwocky will prefer to be
100m or more from roads
Less than 50m from a river
Less than 100m from a swamp
More than 500m from an not-successfully regenerated block
But Jabberwocky will compromise as necessary

31. Coding the Parameters Variable Weight 100 = preferred habitat
Road
0 50 : 12
: 45
: 64
: 100
River
0 50 : 100
: 60
: 40
: 10
Swamp
0 100 : 100
: 66
: 15
NSR
0 100 : 10
: 50
Variable Weight
100 = preferred habitat
75 = good but not great
50 = acceptable
25 = avoided
0 = terrible
Distance Range (m): Weight

32. % of Landscape Devoted to Corridor Under Different Constraints
Stated differently, if you could devote only 1% of the landscape and all four variables were required = red
If you could devote 1% of the landscape and only three variables were required = yellow

33. Projection Model: Pine Beetle
Mountain Pine Beelte Projection
Parameters:
Pine in a suitable biogeoclimatic zone
Stand age >60
Local beetle pressure (powered flight)
Regional beetle pressure (wind transport)
At or below most northerly observed latitude
Observed two years running
KEY POINT
From Adrian Walton: “The model defines the northern latitude of habitat suitability based on the latitude at which the beetle was observed by the AOS [Aerial Overview Survey] in two consecutive years. The model does not recognize those latitudes as “marginally” suitable habitat. In hindsight it may have been more appropriate to choose the northern latitude at which the beetle was observed for three consectuive years.

34. Observed Beetle Kill to Date

35. Projected 2016

36. Projected 2017

37. Projected 2018

38. Projected 2019

39. Projected 2020

40. What’s Missing? Mountain porcupine beetle? Topographical barriers
Mountains tend to get in the way
Fine-scale population data
Marginal vs. optimal habitat
Max. range defined by latitude alone
Vs. effective latitude (incorporating elevation)
No authorship or contact info given…

41. Raster Applications: Site C
Dam is at elevation X; water finds its level
Looking for any pixel at or below the elevation of the dam

42. Summary Raster math works like normal math
(sorry)
Boolean logic is foundational
Remember those Venn diagrams!
Wildlife applications next week

43. Colors: Consider the Following
Red/green colorblindness
8% of men, 0.5% of women
Color maps on B+W printer
Question 3

44. Lightness (Value)
Lightness (value)

45. Saturation
Saturation is a valid answer

46. Hue
Can’t be all of the above

47. Printers vs. Photocopiers
Colorbrewer2.org
B+W photocopiers
Older printers (where I got my trust issues)
Newer printers do better (examples on hand)

48. Take-Home Lightness (Value) “always” works Saturation “should” work
Safest bet if you can’t control the printer
Saturation “should” work
If you’re working in-house
If you’re contracting a print job
Hue sometimes works
Some hues stand out, some don’t
Mileage may vary

49. Scale bar in the wrong units
Scale bar label abbuting labe boundary
North arrow does not agree with provincial boundary
Labels inconsistent and often tiny
What exactly does green mean? Same for both provinces?
Who is the contact person for this map?